Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI

2026-08-04

AI Summary

Fetal brain biometry relies heavily on manual procedures, which are time-consuming and subject to inter-observer variability, while existing automated approaches suffer from limited reproducibility and anatomical interpretability. This work proposes the first fully automated deep learning framework that integrates anatomical landmark localization with geometric optimization, leveraging 3D super-resolution reconstructed fetal brain MRI. The method employs a four-step pipeline to jointly estimate linear measurements and their corresponding landmarks: an initial set of coordinates is regressed from segmentation labels using a 3D CNN, followed by measurement-specific geometric refinement to optimize landmark positions and compute biometric values. Evaluated on two public datasets comprising 150 subjects (gestational age 20–37 weeks), the approach achieves accuracy comparable to or better than the only existing automated method, demonstrating superior robustness, reproducibility, and generalization across scanning protocols—supporting its potential for clinical deployment.

Abstract

Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, and detection of abnormalities. In clinical practice, measurements are manually performed, making them time-consuming and prone to variability. While automated approaches have been proposed, reproducible methods remain limited, particularly those providing anatomically interpretable landmark localization. We present a fully automated deep learning-based framework for reliable and reproducible brain biometry from 3D super-resolution-reconstructed fetal brain MRI. The proposed four-step pipeline derives biometric parameters by jointly estimating linear measurements and their corresponding anatomical landmarks. A 3D convolutional neural network is trained to regress landmark coordinates from brain segmentation label maps, followed by measurement-specific geometric optimization to refine landmark positions and compute measurements. The pipeline is evaluated on two publicly available fetal MRI datasets comprising 150 volumes (gestational age range: 20-37 weeks) acquired across different scanners and protocols, assessing five key biometric measurements across varying acquisition settings and providing a comprehensive evaluation of both measurement accuracy and landmark localization using quantitative metrics and visual assessment. Compared with the only available automated pipeline, the proposed method achieves comparable or improved accuracy for most measurements. In conclusion, we introduce a straightforward pipeline for reliable biometry estimations, with efficiency, interpretability and scalability that support integration into clinical workflows.

Problem

• fetal brain biometry
• reproducibility
• anatomical landmarks
• manual measurement variability
• automated biometry

Innovation

• fetal brain biometry
• deep learning
• anatomical landmark localization
• 3D MRI
• geometric optimization